小说子巴纳克优化是一种以自然为灵感的技术,用于优化电力系统中的反应功率,使用电动汽车
Juntao Zhuang1, Chengwei Wang2, Qiong Cheng3
1Geely Automotive Institute, Hangzhou Vocational & Technical College, Hangzhou, 310000, China. 2021010005@hzvtc.edu.cn.
Scientific reports
|February 20, 2025
概括
一个新的Gooseneck Barnacle优化 (NGBO) 算法有效地管理电动汽车 (EV) 的最佳反应功率调度 (ORPD). 它显著减少了电源系统中的功率损失和电压变化.
科学领域:
- 电气工程 电气工程
- 电力系统优化 电力系统优化
- 计算智能是一种计算智能.
背景情况:
- 越来越多的电动汽车 (EV) 整合给电力系统的稳定性和效率带来了挑战.
- 最佳反应功率调度 (ORPD) 对于管理电力系统至关重要,尤其是在增加电动汽车负载和分布式发电能力的情况下.
- 现有的方法很难有效地解决电动汽车充电时间表和电池状态所带来的复杂性.
研究的目的:
- 为ORPD问题引入一种新的优化算法,即新型Gooseneck Barnacle优化 (NGBO),用于ORPD问题.
- 评估NGBO算法在集成电动汽车的动力系统中的性能.
- 为了证明算法在各种电动汽车透场景下减轻功率损失和电压偏差的能力.
主要方法:
- 开发了新的Gooseneck Barnacle优化 (NGBO) 算法,灵感来自于的繁殖行为.
- 将NGBO算法应用于标准电力系统测试案例 (IEEE 118-和IEEE 57总线系统).
- 在不同的电动汽车透水平下进行模拟,并考虑充电策略.
主要成果:
- 与传统方法相比,NGBO算法成功地减少了高达15%的主动功率损失.
- 使用NGBO算法,电压偏差减少了高达10%.
- 在处理与EV相关的不确定性方面,NGBO算法在现有元启发式技术上表现优越.
结论:
- 新的Gooseneck Barnacle优化 (NGBO) 算法是一种有效的工具,用于解决具有EV集成的电力系统中的ORPD问题.
- 在减少功率损失和电压偏差方面,NGBO提供了显著的改进,提高了电力系统的整体效率和稳定性.
- 该算法的生物启发方法为未来的电力系统优化研究提供了一个有希望的方向.
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